Social Network Cross-Category Influencer Impact Analysis

DOI

10.2991/978-94-6463-866-0_66How to use a DOI?

Keywords

cross-category influencers; engagement analysis; brand collaborations; fake engagement detection; network influence; virality trends; graph analytics; influencer marketing

Abstract

Influencers often transcend single content niches, impacting diverse audiences across categories. This study investigates cross-category influencers to understand their role in engagement dynamics, brand collaborations, and network influence. Unlike traditional approaches focused on follower counts, we integrate graph analytics, statistical analysis, and machine learning to detect fake engagement, uncover brand-influencer relationships, rank influencers by network impact, and analyze virality patterns. Our work introduces a holistic framework for evaluating influencer effectiveness across domains, offering deeper insights for optimizing digital marketing strategies.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - D. Punitha
AU  - B. Madhumita
AU  - C. S. Chinmayi
AU  - Abishek Senthil Kumar
PY  - 2025
DA  - 2025/10/31
TI  - Social Network Cross-Category Influencer Impact Analysis
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 812
EP  - 821
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_66
DO  - 10.2991/978-94-6463-866-0_66
ID  - Punitha2025
ER  -